U.S. patent application number 15/427279 was filed with the patent office on 2018-08-09 for method of monitoring processing system for processing substrate.
The applicant listed for this patent is UNITED MICROELECTRONICS CORP.. Invention is credited to Chia-Chi Chang, Chiu-Ping Chang, Meng-Chih Chang, Feng-Chi Chung, Ching-Hsing Hsieh, Li-Ting Lin, Lian-Hua Shih, Ming-Tung Wang, Yung-Yu Yang.
Application Number | 20180224817 15/427279 |
Document ID | / |
Family ID | 63037752 |
Filed Date | 2018-08-09 |
United States Patent
Application |
20180224817 |
Kind Code |
A1 |
Shih; Lian-Hua ; et
al. |
August 9, 2018 |
METHOD OF MONITORING PROCESSING SYSTEM FOR PROCESSING SUBSTRATE
Abstract
A method of monitoring a processing system for processing a
substrate is provided. The method includes the following steps:
acquiring data from the processing system for a plurality of
parameters, the data including a plurality of data values; grouping
the parameters into a plurality of sub-groups, each of the
sub-groups including a plurality of correlated parameters;
constructing a principle components analysis (PCA) model from the
data values for the correlated parameters in a first one of the
sub-groups, including normalizing the data values in the first one
of the sub-groups with a first weighting factor and a second
weighting factor, wherein the first weighting factor is different
from the second weighting factor; and determining a statistical
quantity using the PCA model.
Inventors: |
Shih; Lian-Hua; (Chiayi
City, TW) ; Chang; Chia-Chi; (Tainan City, TW)
; Lin; Li-Ting; (Tainan City, TW) ; Hsieh;
Ching-Hsing; (Zhubei City, TW) ; Chung; Feng-Chi;
(Zhunan Township, TW) ; Chang; Meng-Chih;
(Kaohsiung City, TW) ; Wang; Ming-Tung; (Jiadong
Township, TW) ; Chang; Chiu-Ping; (Kaohsiung City,
TW) ; Yang; Yung-Yu; (Tainan City, TW) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
UNITED MICROELECTRONICS CORP. |
Hsinchu |
|
TW |
|
|
Family ID: |
63037752 |
Appl. No.: |
15/427279 |
Filed: |
February 8, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G05B 23/024
20130101 |
International
Class: |
G05B 19/048 20060101
G05B019/048; G06F 17/30 20060101 G06F017/30; G05B 17/02 20060101
G05B017/02 |
Claims
1. A method of monitoring a processing system for processing a
substrate, comprising: acquiring data from the processing system
for a plurality of parameters, the data comprising a plurality of
data values; grouping the parameters into a plurality of
sub-groups, each of the sub-groups comprising a plurality of
correlated parameters; constructing a principle components analysis
(PCA) model from the data values for the correlated parameters in a
first one of the sub-groups, comprising: normalizing the data
values in the first one of the sub-groups with a first weighting
factor and a second weighting factor, wherein the first weighting
factor is different from the second weighting factor; and
determining a statistical quantity using the PCA model.
2. The method according to claim 1, wherein normalizing the data
values in the first one of the sub-groups comprises: applying the
first weighting factor to a first one of the data values and the
second weighting factor to a second one of the data values in the
first one of the sub-groups.
3. The method according to claim 1, further comprising: determining
a control limit for the statistical quantity; and comparing the
statistical quantity to the control limit, wherein a process fault
has occurred when the statistical quantity exceeds the control
limit.
4. The method according to claim 1, wherein each one of the
sub-groups comprises less than ten correlated parameters.
5. The method according to claim 1, wherein the data comprises at
least one of a temperature of a heat exchanger, a resistivity of a
heat exchanger, a distance from a heater to a shower head, a power
output of a heater, a temperature reading of a heater, an RF
forward power, an RF impedance, an electrode voltage, an RF
reflective power, an argon flow rate, a helium flow rate during an
atom transfer radical polymerization (ATRP), a helium flow rate of
an oxygenator, an oxygen flow rate, a helium flow rate, a flow rate
of an oxygenator, a flow rate of an atom transfer radical
polymerization, a chamber pressure reading, a position of a
throttle valve, and a chamber pressure supplied.
6. The method according to claim 1, wherein the statistical
quantity is a Hotelling T.sup.2 parameter.
7. A method of monitoring a processing system for processing a
substrate, comprising: acquiring data from the processing system
for a plurality of parameters in a first period of time, the data
comprising a plurality of data values; grouping the parameters into
a plurality of sub-groups, each of the sub-groups comprising a
plurality of correlated parameters; constructing a principle
components analysis (PCA) model from a first set of data values for
the correlated parameters in a first one of the sub-groups;
determining a statistical quantity using the PCA model; performing
a prevention maintenance step to the processing system after the
first period of time; acquiring a second set of data values for the
correlated parameters in the first one of the sub-groups from the
processing system in a second period of time after performing the
prevention maintenance step; and determining an updated statistical
quantity using a combination of the second set of data values and
the PCA model.
8. The method according to claim 7, wherein the prevention
maintenance step comprises at least one of cleaning the processing
system, replacing a component of the processing system, and
repairing a component of the processing system.
9. The method according to claim 7, wherein constructing the PCA
model comprises: normalizing the first set of data values in the
first one of the sub-groups with a first weighting factor and a
second weighting factor, wherein the first weighting factor is
different from the second weighting factor.
10. The method according to claim 9, wherein constructing the PCA
model further comprises: applying the first weighting factor to a
first one of the first set of data values and the second weighting
factor to a second one of the first set of data values in the first
one of the sub-groups.
11. The method according to claim 7, further comprising:
determining a control limit for the updated statistical quantity;
and comparing the updated statistical quantity to the control
limit, wherein a process fault has occurred when the updated
statistical quantity exceeds the control limit.
12. The method according to claim 7, wherein each one of the
sub-groups comprises less than ten correlated parameters.
13. The method according to claim 7, wherein the data comprises at
least one of a temperature of a heat exchanger, a resistivity of a
heat exchanger, a distance from a heater to a shower head, a power
output of a heater, a temperature reading of a heater, an RF
forward power, an RF impedance, an electrode voltage, an RF
reflective power, an argon flow rate, a helium flow rate during an
atom transfer radical polymerization (ATRP), a helium flow rate of
an oxygenator, an oxygen flow rate, a helium flow rate, a flow rate
of an oxygenator, a flow rate of an atom transfer radical
polymerization, a chamber pressure reading, a position of a
throttle valve, and a chamber pressure supplied.
14. The method according to claim 7, wherein the statistical
quantity is a Hotelling T.sup.2 parameter.
15. The method according to claim 7, wherein the second set of data
values comprise a first group of data values and a second group of
data values acquired subsequent to the first group of data values,
the method further comprising: determining whether a current data
value of the second group of data values exceeds an estimated
statistical quantity or not, wherein the estimated statistical
quantity is determined using a combination of precious data values
of the second set of data values acquired in the second period of
time and the PCA model; and constructing an updated PCA model using
a combination of the current data value, the previous data values
and the PCA model for determining the updated statistical quantity
when the current data value does not exceed the estimated
statistical quantity.
Description
BACKGROUND
Technical Field
[0001] The disclosure relates in general to a method of monitoring
a processing system for processing a substrate, and more
particularly to a method of monitoring a processing system for
processing a substrate using a principle components analysis (PCA)
model.
Description of the Related Art
[0002] In the whole manufacturing process, various processing
parameters are required to be monitored and controlled for fault
detections. However, difficulties of encountering large amount of
various data parameters may be time-consuming and require a large
number of monitor checks. Therefore, there is a desire in reducing
the loading of monitoring and fault detections of manufacturing
processes.
SUMMARY OF THE INVENTION
[0003] The disclosure is directed to a method of monitoring a
processing system for processing a substrate. According to the
embodiments of the present disclosure, by grouping processing
parameters into sub-groups of correlated parameters and applying
different weighting factors on different correlated parameters in
one sub-group when constructing a PCA model for determining the
statistical quantity, the statistical quantity provides a higher
sensitivity to fault detection and an improved process control.
[0004] According to an embodiment of the present disclosure, a
method of monitoring a processing system for processing a substrate
is disclosed. The method includes the following steps: acquiring
data from the processing system for a plurality of parameters, the
data including a plurality of data values; grouping the parameters
into a plurality of sub-groups, each of the sub-groups including a
plurality of correlated parameters; constructing a principle
components analysis (PCA) model from the data values for the
correlated parameters in a first one of the sub-groups, including
normalizing the data values in the first one of the sub-groups with
a first weighting factor and a second weighting factor, wherein the
first weighting factor is different from the second weighting
factor; and determining a statistical quantity using the PCA
model.
[0005] According to another embodiment of the present disclosure, a
method of monitoring a processing system for processing a substrate
is disclosed. The method includes the following steps: acquiring
data from the processing system for a plurality of parameters in a
first period of time, the data including a plurality of data
values; grouping the parameters into a plurality of sub-groups,
each of the sub-groups including a plurality of correlated
parameters; constructing a principle components analysis (PCA)
model from a first set of data values for the correlated parameters
in a first one of the sub-groups; determining a statistical
quantity using the PCA model; performing a prevention maintenance
step to the processing system after the first period of time;
acquiring a second set of data values for the correlated parameters
in the first one of the sub-groups from the processing system in a
second period of time after performing the prevention maintenance
step; and determining an updated statistical quantity using a
combination of the second set of data values and the PCA model.
[0006] The disclosure will become apparent from the following
detailed description of the preferred but non-limiting embodiments.
The following description is made with reference to the
accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 shows a processing system according to an embodiment
of the present disclosure;
[0008] FIG. 2 presents a method of monitoring a processing system
for processing a substrate according to an embodiment of the
present disclosure;
[0009] FIG. 3 presents a method of monitoring a processing system
for processing a substrate according to another embodiment of the
present disclosure;
[0010] FIG. 4A presents a statistical quantity with respect to
substrate runs according to an embodiment of the present
disclosure;
[0011] FIGS. 4B-4C respectively show measured impedance values of a
single parameter with respect to substrate runs according to an
embodiment of the present disclosure;
[0012] FIG. 5A shows measured film uniformity with respect to
substrate runs according to an embodiment of the present
disclosure;
[0013] FIG. 5B shows a schematic drawing of a substrate surface;
and
[0014] FIG. 6 presents a statistical quantity with respect to
substrate runs according to another embodiment of the present
disclosure.
DETAILED DESCRIPTION OF THE INVENTION
[0015] According to the embodiments of the present disclosure, by
grouping processing parameters into sub-groups of correlated
parameters and applying different weighting factors on different
correlated parameters in one sub-group when constructing a PCA
model for determining the statistical quantity, the statistical
quantity provides a higher sensitivity to fault detection and an
improved process control. The identical or similar elements of the
embodiments are designated with the same reference numerals. It is
to be noted that the drawings are simplified for clearly describing
the embodiments, and the details of the structures of the
embodiments are for exemplification only, not for limiting the
scope of protection of the disclosure. Ones having ordinary skills
in the art may modify or change the structures according to the
embodiments of the present disclosure.
[0016] According to an embodiment of the present disclosure, a
semiconductor processing system 1 is provided. FIG. 1 shows a
processing system according to an embodiment of the present
disclosure. In the embodiment, the semiconductor processing system
1 may include a process tool 10 and a process performance
monitoring system 100, and the process performance monitoring
system 100 may include a plurality of sensors 20 and a controller
30. Substrates/wafers are transferred into the process tool 10 to
be processed. The sensors 20 are coupled to the process tool 10 to
measure data, and the controller 30 is coupled to the sensors 20 to
receive data. The controller 30 may also be further coupled to the
process tool 10. In the embodiments, the controller 30 is
configured to monitor process performance data for process fault
detection of the processes substrates/wafers.
[0017] FIG. 2 presents a method of monitoring a processing system
for processing a substrate according to an embodiment of the
present disclosure.
[0018] First, at step S101, the method starts with acquiring data
from the processing system for a plurality of parameters, the data
including a plurality of data values. The processing system may be
a film deposition system, a CVD system, a PVD system, an etching
system, a plasma system, and etc. The data from the processing
system can be acquired using a plurality of sensors, as shown in
FIG. 1, coupled to the processing system and a controller.
[0019] In the embodiments, the data may include at least one of the
following: a temperature of a heat exchanger, a resistivity of a
heat exchanger, a distance from a heater to a shower head, a power
output of a heater, a temperature reading of a heater, an RF
forward power, an RF impedance, an electrode voltage, an RF
reflective power, an argon flow rate, a helium flow rate during an
atom transfer radical polymerization (ATRP), a helium flow rate of
an oxygenator, an oxygen flow rate, a helium flow rate, a flow rate
of an oxygenator, a flow rate of an atom transfer radical
polymerization, a chamber pressure reading, a position of a
throttle valve, and a chamber pressure supplied, but not limited
thereto. For example, the data may include the data value(s) of at
least one of the above parameters.
[0020] Next, as shown in FIG. 2, at step S102, the parameters are
grouped into a plurality of sub-groups, each of the sub-groups
including a plurality of correlated parameters. For example, the
above mentioned parameters can be grouped into three sub-groups
including a heater sub-group, an RF sub-group and a pressure
sub-group, which will be illustrated in Table 1 hereinafter. The
parameters in the same sub-group are correlated. That is, adjusting
one parameter may correspondingly influence the data value of
another parameter in the same sub-group, and an observed change of
the data value in one parameter may be resulted from an adjustment
of another parameter in the same sub-group. For example, when a
power output of a heater is changed, the temperature of the heater
is correspondingly changed, and thus the data values of these two
parameters are considered correlated and can be monitored
together.
[0021] In some embodiments, after a large number of parameters of
the processing system are grouped into sub-groups, each one of the
sub-groups includes less than ten correlated parameters.
[0022] Next, at step S103, a principle components analysis (PCA)
model is constructed from the data values for the correlated
parameters in a first one of the sub-groups. That is, at this step,
a PCA model is not construed from all of the acquired data values
of all of the parameters; instead, a PCA model is constructed from
only the data values of the correlated parameters in one sub-group.
In some embodiments, the PCA model may be constructed by utilizing
various commercial available tools/software, such as MATLAB or R
Language, but not limited thereto.
[0023] At the present step, constructing the PCA model further
includes normalizing the data values in the first one of the
sub-groups with a first weighting factor and a second weighting
factor, wherein the first weighting factor is different from the
second weighting factor.
[0024] In some embodiments, the normalization of the data values in
the first one of the sub-groups may include applying the first
weighting factor to a first one of the data values and the second
weighting factor to a second one of the data values in the first
one of the sub-groups. That is, the data values of different
correlated parameters in the same sub-group are normalized with at
least two different weighting factors. In some embodiments, the
normalization is based on the following equation for each of the
correlated parameters in one sub-group, and the normalized result
Z' can be expressed by the following equation:
Z'=(P/Pa)/(Z*.delta.);
[0025] where P represents a measured data value of a parameter, Pa
represents an average value of the data values of the parameter, Z
represents the weighting factor for the parameter, and .delta.
represents the standard deviation of the data values of the
parameter.
[0026] When the data values of the correlated parameters in a
sub-group are normalized applying the above equation, each of the
parameters may be applied with different weighting factors
according to the predetermined significance of each of the
parameters. The smaller the Z value a parameter is applied with,
the more significantly the parameter is contributed to the PCA
model; in other words, the PCA model is more sensitive to the data
values of the parameter with a smaller Z value.
[0027] Next, at step S104, a statistical quantity is determined
using the PCA model. In some embodiments, the statistical quantity
may be a Hotelling T.sup.2 parameter.
[0028] Next, a control limit may be further determined for the
statistical quantity, and the statistical quantity may be compared
to the control limit, wherein a process fault has occurred when the
statistical quantity exceeds the control limit. When a process
fault is detected, an operator can be notified.
[0029] According to the embodiments of the present disclosure, the
statistical quantity can be used as a processing system health
index for fault detection and process control. Particularly, by
grouping processing parameters into sub-groups of correlated
parameters and applying different weighting factors on different
correlated parameters in one sub-group when constructing a PCA
model for determining the statistical quantity, the statistical
quantity provides a higher sensitivity to fault detection and an
improved process control.
[0030] FIG. 3 presents a method of monitoring a processing system
for processing a substrate according to another embodiment of the
present disclosure.
[0031] As shown in FIG. 3, at step S201, the method starts with
acquiring data from the processing system for a plurality of
parameters in a first period of time, the data including a
plurality of data values; next, at step S202, the parameters are
grouped into a plurality of sub-groups, each of the sub-groups
including a plurality of correlated parameters. These two steps are
substantially the same as the previously-described steps S101 and
S102, and the descriptions are omitted.
[0032] Next, at step S203, a principle components analysis (PCA)
model is constructed from a first set of the data values for the
correlated parameters in a first one of the sub-groups. That is, at
this step, a PCA model is not construed from all of the acquired
data values of all of the parameters; instead, a PCA model is
constructed from only the first set of the data values of the
correlated parameters in one sub-group acquired in the first period
of time. In some embodiments, the PCA model may be constructed by
utilizing various commercial available tools, such as MATLAB or R
Language, but not limited thereto.
[0033] At the present step, constructing the PCA model may
optionally further includes normalizing the first set of the data
values in the first one of the sub-groups with a first weighting
factor and a second weighting factor, wherein the first weighting
factor is different from the second weighting factor. This optional
normalization operation is substantially the same as the
previously-described normalization operation at step S103, and the
descriptions are omitted.
[0034] Next, at step S204, a statistical quantity is determined
using the PCA model. In some embodiments, the statistical quantity
may be a Hotelling T.sup.2 parameter.
[0035] Next, at step S205, a prevention maintenance step is
performed to the processing system after the first period of time.
In some embodiments, the prevention maintenance step may include at
least one of cleaning the processing system, replacing a component
of the processing system, and repairing a component of the
processing system.
[0036] Next, at step S206, a second set of data values for the
correlated parameters in the first one of the sub-groups are
acquired from the processing system in a second period of time
after performing the prevention maintenance step. That is, after
the first set of data values for the correlated parameters in one
sub-group are acquired in the first period of time and a
statistical quantity is determined from the first set of data
values using a PCA model in the first period of time, followed by
performing the prevention maintenance step after the first period
of time, a second set of data values of the same correlated
parameters in the same sub-group are acquired in a second period of
time, which is subsequent to the first period of time.
[0037] Next, at step S207, an updated statistical quantity is
determined using a combination of the second set of data values and
the PCA model. More specifically, the updated statistical quantity
is determined using a combination of the second set of data values
acquired in the second period of time and the PCA model constructed
from the first set of data values acquired in the first period of
time, and the first set of data values and the second set of values
are for the same correlated parameters in the same sub-group. In
other words, the original statistical quantity and the updated
statistical quantity both refer to the same correlated parameters
in the same sub-group; while the original statistical quantity
obtained from the data values collected in the first period of time
and the prevention maintenance step may cause a shift in value of
the statistical quantity, the updated statistical quantity obtained
incorporating the second set of data values acquired in the second
period of time after the prevention maintenance step provides an
auto-corrected index for the fault detection of the correlated
parameters in the same sub-group after the prevention maintenance
step is performed.
[0038] In the embodiments, the method of the present disclosure may
optionally further include the followings. In an embodiment, the
second set of data values may include a first group of data values
and a second group of data values acquired subsequent to the first
group of data values, and the method may further include
determining whether a current data value of the second group of
data values exceeds an estimated statistical quantity or not,
wherein the estimated statistical quantity is determined using a
combination of precious data values of the second set of data
values acquired in the second period of time and the PCA model; and
constructing an updated PCA model using a combination of the
current data value, the previous data values and the PCA model for
determining the updated statistical quantity when the current data
value does not exceed the estimated statistical quantity.
[0039] In other words, acquiring the second set of data values
includes acquiring a first group of data values and then acquiring
a second group of data values. In the embodiments, an estimated
statistical quantity is determined using the first group of data
values of the second set of data values acquired in the second
period of time, the PCA model construed in the first period of
time, and optionally at least one data value, which is the
aforementioned precious data values, from the second group of data
values of the second set of data values. With every additional data
value, which is the aforementioned current data value, acquired, an
updated PCA model is further constructed for the current processing
system as long as the acquired additional data value does not
exceed the current estimated statistical quantity, and a renew
estimated statistical quantity is further determined. On the
contrary, if the current data value exceeds the current estimated
statistical quantity, the current data value will be abandoned, and
another additional data value will be acquired, and an updated PCA
model will be constructed if this another additional data value
does not exceed the current estimated statistical quantity. This
process can repeat multiple times to keep updating the updated PCA
model with more additional data values of the second set of data
values acquired, and this repeating process is called a "training
set," in which the updated PCA model is constantly and slightly
changing and auto-refreshing. When the "training set" ends, instead
of obtaining another renew estimated statistical quantity, an
updated statistical quantity is determined from the updated PCA
model constructed using a combination of the first group and the
second group of the second set of data values acquired in the
second period of time and the PCA model constructed in the first
period of time.
[0040] Next, a control limit may be further determined for the
updated statistical quantity, and the updated statistical quantity
may be compared to the control limit, wherein a process fault of
the current processing system, which is after performing the
prevention maintenance step, has occurred when the updated
statistical quantity exceeds the control limit. When a process
fault is detected, an operator can be notified.
[0041] According to the embodiments of the present disclosure, the
updated statistical quantity can be used as a processing system
health index for fault detection and process control after
performing a prevention maintenance step. Particularly, by
auto-refreshing updated PCA model(s) from a combination of the
second set of data values acquired in the second period of time
after the prevention maintenance step and the PCA model constructed
from the first set of data values acquired in the first period of
time, the updated statistical quantity can be determined for
providing a more accurate fault detection and improved process
control.
[0042] Further explanation is provided with the following examples.
However, the following examples are for purposes of describing
particular embodiments only, and are not intended to be limiting.
Table 1 shows some exemplary examples of processing parameters and
the sub-groups of correlated parameters. It is to be noted that the
following parameters are for examples only, and the present
disclosure is not limited thereto.
TABLE-US-00001 TABLE 1 Parameter Description HEATER Sub-group
HEAT_EXCHANGER_TEMP Temperature of heat exchanger
HEAT_EXCHANGER_RESISTIVITY Resistivity of heat exchanger
SIDE1,2_HEATER_CURRENT_DISTANCE_FROM_SHOWER_HEAD distance from
heater to shower head SIDE1,2_HEATER_POWER + OUTPUT Power output of
heater SIDE1,2_HEATER_TEMP_READING Temperature reading of heater RF
Sub-group SIDE1,2_HRF_FORWARD_POWER RF forward power
SIDE1,2_HRF_IMPEDANCE_R RF impedance (Reflective)
SIDE1,2_HRF_IMPEDANCE_I RF impedance (Input)
SIDE1,2_HRF_ELECTRODE_BIAS Electrode voltage
SIDE1,2_HRF_REFLECTED_POWER_COEFFICIENT RF reflective power
Pressure Sub-group AR_CURRENT_FLOW Ar flow rate
HE_ATRP_CURRENT_FLOW He flow rate at during atom transfer radical
polymerization HE_MDEOS_CURRENT_FLOW He flow rate of oxygenator
O2_LOW_CURRENT_FLOW O.sub.2 flow rate HE_CURRENT_FLOW He flow rate
MDEOS_CURRENT_FLOW Flow rate of oxygenator ATRP_CURRENT_FLOW Flow
rate of atom transfer radical polymerization PRESSURE_READING
Chamber pressure reading THROTTLE_VALVE_POS Position of throttle
valve PRESSURE Chamber pressure supplied
[0043] FIG. 4A presents a statistical quantity with respect to
substrate runs according to an embodiment of the present
disclosure, and FIGS. 4B-4C respectively show measured impedance
values of a single parameter with respect to substrate runs
according to an embodiment of the present disclosure. In the
embodiment as shown in FIGS. 4A-4C, the RF Sub-group is taken as an
example. In FIGS. 4A-4C, the y-axis represents substrate runs, and
the number of substrates runs increases toward where the arrows
point at; in other words, the number of substrate runs increases
from the left side to the right side in FIGS. 4A-4C, and the data
points of the curves on the right side belong to the substrates
processed later in time than the substrates with the data points of
the curves on the left side in the drawings.
[0044] As shown in FIGS. 4B and 4C, two types of impedance utilized
in the processing system are monitored individually and two
monitoring charts are presented. As shown in FIGS. 4B-4C, in the
regions R2 and R3, the variance of impedance values are relatively
minor to be detected and notified for sending alarm messages. On
the contrary, as shown in FIG. 4A, the statistical quantity of the
RF sub-group incorporating five correlated parameters of RF forward
power, RF impedance (R), RF impedance (I), electrode voltage and RF
reflect power is determined using a PCA model constructed from the
data values of the above five correlated parameters with different
weighting factors applied, the sensitivity to fault detection is
greatly increased. As shown in FIG. 4A, the statistical quantity is
determined to be 0.84, control limits with different tolerance of
0.79, 0.74 and 0.69 are further determined, and the values in the
region R1 dramatically drops below the outlier limit of 0.69
indicating a fault detected.
[0045] In order to further verify the monitoring method of the
embodiments of the present disclosure, the film thicknesses of the
substrate runs of the embodiment in FIGS. 4A-4C are measured, and
the substrate notified with a fault occurred is further observed.
FIG. 5A shows measured film uniformity with respect to substrate
runs according to the embodiment in FIGS. 4A-4C, and FIG. 5B shows
a schematic drawing of the observed substrate surface which has
been notified with a fault occurred as shown in FIG. 4A.
[0046] As shown in FIG. 5A, the average film thickness of the
substrates is about 150 .ANG., control limits with different
tolerance are determined to be 175 .ANG., 200 .ANG. and 225 .ANG.,
respectively. As shown in FIG. 5A, in the region R4, the film
thickness jumps up to exceed even the outlier limit, and the
measured value is 369 .ANG.. Moreover, as shown in FIG. 5B, two
large particles 510 are found to be located on the surface of the
substrate 500. Accordingly, FIGS. 5A-5B prove that the fault
detection is accurate and very sensitive to even minor variance in
processing conditions.
[0047] FIG. 6 presents a statistical quantity with respect to
substrate runs according to another embodiment of the present
disclosure. To give a preview, FIG. 6 shows an example of
auto-refreshing PCA models with applying a "training set" to
further tune and update the PCA model.
[0048] As shown in FIG. 6, after a prevention maintenance step PM
is performed, the second period of time starts. In the second
period of time, a first group G1 of data values P1-P9 is acquired,
and a second group G2 of data values P10-P25 is further acquired.
In the embodiment, the second group of data values starts from data
value P10, which is defined as a current data value, an estimated
statistical quantity is determined using the first group of data
values P1-P9, the current data value P10 does not exceed the
estimated statistical quantity determined from data values P1-P9,
and then an updated PCA model is constructed using the current data
value P10, previous data values P1-P9 and the PCA model constructed
before the prevention maintenance step PM.
[0049] Next, a current data value P11 is acquired, a renew
estimated statistical quantity is further determined using the
previous data values P1-P10, the current data value P11 does not
exceed the renew estimated statistical quantity determined from
data values P1-P10, and then a further updated PCA model is
constructed using the current data value P11, previous data values
P1-P10 and the PCA model constructed before the prevention
maintenance step PM. This process can repeat multiple times to keep
updating and tuning the updated PCA model with more additional data
values of the second set of data values acquired, and this
repeating process is called a "training set," in which the updated
PCA model is constantly and slightly changing and auto-refreshing.
In the "training set", since the estimated statistical quantity
varies with every additional data value acquired, the control
limits determined according thereto vary as well. For example, as
shown in FIG. 6, the out-of-control limit OOC, the out-of-warning
limit OOW and the outlier limit OL vary while tuning the PCA
model.
[0050] Moreover, in the embodiment as shown in FIG. 6, the current
data value P17 exceeds the current estimated statistical quantity
determined from data values P1-P16, the current data value P17 will
be abandoned, and another additional data value P18 will be
acquired. Because the data value P18 does not exceed the current
estimated statistical quantity determined from data values P1-P16,
an updated PCA model will be constructed using the current data
value P18, previous data values P1-P16 (excluding the data value
P17) and the PCA model constructed before the prevention
maintenance step PM.
[0051] In the embodiment as shown in FIG. 6, the "training set"
ends at data value P25. Thus, instead of obtaining another renew
estimated statistical quantity, an updated statistical quantity is
determined from the updated PCA model constructed using a
combination of the first group G1 and the second group G2 of the
second set of data values P1-P25 acquired in the second period of
time and the PCA model constructed in the first period of time, and
monitoring and fault detection of the processing system according
to the updated statistical quantity starts from data value P26. It
is to be noted that the number of data values in the first group G1
and the second group G2 may vary according to actual needs and are
not limited thereto.
[0052] While the invention has been described by way of example and
in terms of the preferred embodiment(s), it is to be understood
that the invention is not limited thereto. On the contrary, it is
intended to cover various modifications and similar arrangements
and procedures, and the scope of the appended claims therefore
should be accorded the broadest interpretation so as to encompass
all such modifications and similar arrangements and procedures.
* * * * *